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Production planning of multi-stage multi-option seru production systems with sustainable measures

JOURNAL OF CLEANER PRODUCTION 2015年 105卷 285-299页

作者： Liu, ChenGuang Dang, Fen Li, WenJuan Lian, Jie Evans, Steve Yin, Yong Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Xian Univ Technol Sch Econ & Management Xian 710048 Peoples R China Univ Cambridge Inst Mfg Cambridge CB3 0FS England Yamagata Univ Dept Econ & Business Management Yamagata 9908560 Japan

With the concerns over climate change and the escalation in worldwide population, sustainable development attracts more and more attention of academia, policy makers, and businesses in countries. Sustainable manufacturing is an inextricable measure to achieve sustainable development since manufacturing is one of the main energy consumers and greenhouse gas contributors. In the previous researches on production planning of manufacturing systems, environmental factor was rarely considered. This paper investigates the production planning problem under the performance measures of economy and environment with respect to seru production systems, a new manufacturing system praised as Double E (ecology and economy) in Japanese manufacturing industries. We propose a mathematical model with two objectives minimizing carbon dioxide emission and makespan for processing all product types by a seru production system. To solve this mathematical model, we develop an algorithm based on the non-dominated sorting genetic algorithm ii. The computation results and analysis of three numeral examples confirm the effectiveness of our proposed algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Sustainable manufacturing Seru production Operational decision-making non-dominated sorting genetic algorithm ii

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Optimal Features Selected by NSGA-ii for Partial Discharge Pulses Separation Based on Time-frequency Representation and Matrix Decomposition

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2013年第3期20卷 825-838页

作者： Wang, Ke Liao, Ruijin Yang, Lijun Li, Jian Grzybowski, Stanislaw Hao, Jian Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Mississippi State Univ Dept Elect & Comp Engn High Voltage Lab Mississippi State MS 39762 USA Chongqing Elect Power Res Inst Chongqing 401123 Peoples R China

This paper presents a feature extraction algorithm for partial discharge (PD) pulses separation using S transform (ST)-based time-frequency representation. Firstly, the algorithm acquires a series of base vectors in the frequency domain and location vectors in the time domain obtained by applying a non-negative matrix factorization (NMF)-based matrix decomposition technique to compress ST amplitude (STA) matrices of PD pulses. Then, a new group of features including sharpness, sum of derivatives, sparsity, entropy, mean value and standard deviation is extracted from the base and location vectors, which is further separated by a fuzzy C-means (FCM) clustering algorithm. Finally, non-dominated sorting genetic algorithm ii (NSGA-ii) is introduced as a feature selection tool to improve the FCM clustering performance and acquire the corresponding selected feature subsets. The 600 PD pulses sampled from four typical defect models are adopted for testing. It is shown that a minimum clustering error of 7.67% with 4 dimensional optimal feature subset selected by NSGA-ii is achieved when NMF parameter r = 1. In addition, NSGA-ii can not only reduce the feature dimension but also dramatically improve the FCM clustering performance compared with the original extracted features. The selected four features are also examined by the data of two PD sources simultaneous active. The results demonstrate that it is feasible to apply the proposed algorithm to PD pulses separation.

关键词： Partial discharge pulses separation S transform non-negative matrix factorization non-dominated sorting genetic algorithm ii

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Reduction in the nitrogen oxide and soot emissions in a diesel engine combustion system using an approximate optimization method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2012年第D12期226卷 1707-1718页

作者： Lee, Seung Joo Park, Jae In Lee, Soo Hong Lee, Joon Kyu Lee, Jongsoo Yonsei Univ Sch Mech Engn Seoul 120749 South Korea Hyundai Kia Motors Powertrain CAE Team Gyeonggi South Korea

The present study explores how to optimize a common-rail diesel engine to minimize nitrogen oxide and soot emissions with constraints on hydrocarbon and carbon monoxide emissions. A numerical diesel engine combustion and emission analysis is conducted using the zero-dimensional model. The simulation data are approximated, which accommodates the combustion characteristics of common-rail diesel engines and exhaust. The back-propagation neural network is used not only as a global function approximation tool for predicting non-linear characteristics but also for a global sensitivity analysis to examine causality between the input variables and the output responses. Given an engine operating condition, constrained bi-objective Pareto-optimal solutions are identified by the non-dominated sorting genetic algorithm ii. Optimal designs to minimize the nitrogen oxide and soot emissions subject to the hydrocarbon and carbon monoxide emissions requirements are identified. By comparing the number of Pareto solutions with a baseline design, a reduction in both nitrogen oxide emissions and soot emissions is achieved and these back-propagation-neural-network-based approximate designs are verified using the actual zero-dimensional model data.

关键词： Diesel engine combustion system nitrogen oxide and soot emissions zero-dimensional model back-propagation neural network approximate optimization non-dominated sorting genetic algorithm ii

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Task scheduling using NSGA ii with fuzzy adaptive operators for computational grids

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2014年第5期74卷 2333-2350页

作者： Salimi, Reza Motameni, Homayun Omranpour, Hesam Tabari Univ Babol Coll Comp Sci Babol Sar Iran Islamic Azad Univ Sari Branch Dept Comp Engn Sari Iran

Scheduling algorithms have an essential role in computational grids for managing jobs, and assigning them to appropriate resources. An efficient task scheduling algorithm can achieve minimum execution time and maximum resource utilization by providing the load balance between resources in the grid. The superiority of genetic algorithm in the scheduling of tasks has been proven in the literature. In this paper, we improve the famous multi-objective genetic algorithm known as NSGA-ii using fuzzy operators to improve quality and performance of task scheduling in the market-based grid environment. Load balancing, Makespan and Price are three important objectives for multi-objective optimization in the task scheduling problem in the grid. Grid users do not attend load balancing in making decision, so it is desirable that all solutions have good load balancing. Thus to decrease computation and ease decision making through the users, we should consider and improve the load balancing problem in the task scheduling indirectly using the fuzzy system without implementing the third objective function. We have used fuzzy operators for this purpose and more quality and variety in Pareto-optimal solutions. Three functions are defined to generate inputs for fuzzy systems. Variance of costs, variance of frequency of involved resources in scheduling and variance of genes values are used to determine probabilities of crossover and mutation intelligently. Variance of frequency of involved resources with cooperation of Makespan objective satisfies load balancing objective indirectly. Variance of genes values and variance of costs are used in the mutation fuzzy system to improve diversity and quality of Pareto optimal front. Our method conducts the algorithm towards best and most appropriate solutions with load balancing in less iteration. The obtained results have proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality. (C) 2014 Elsevier I

关键词： Task scheduling Load balancing Grid computing non-dominated sorting genetic algorithm ii Variance-based fuzzy operators Multi-objective optimization

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Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2016年第3期17卷 327-337页

作者： Cao, Jia Yan, Zheng He, Guangyu Shanghai Jiao Tong Univ Dept Elect Engn Minist Educ Key Lab Control Power Transmiss & Convers Shanghai Peoples R China

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm ii (NSGAii) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAii method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

关键词： ac/dc hybrid system multi-objective human learning optimization method multi-objective optimal power flow pareto front non-dominated sorting genetic algorithm ii

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Optimum multi-drug regime for compartment model of tumour: cell-cycle-specific dynamics in the presence of resistance

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JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS 2021年第4期48卷 543-562页

作者： Panjwani, Bharti Singh, Vijander Rani, Asha Mohan, Vijay Univ Delhi Netaji Subhas Inst Technol Delhi India Banasthali Vidyapith Sch Automat Tonk Rajasthan India

This work is focused on multi-objective optimisation of a multi-drug chemotherapy schedule for cell-cycle-specific cancer treatment under the influence of drug resistance. The acquired drug resistance to chemotherapeutic agents is incorporated into the existing compartmental model of breast cancer. Furthermore, the toxic effect of drugs on healthy cells and overall drug concentration in the patient body are also constrained in the proposed model. The objective is to determine the optimal drug schedule according to the patient's physiological condition so that the tumour burden is minimised. A multi-objective optimisation algorithm, non-dominated sorting genetic algorithm-ii (NSGA-ii) is utilised to solve the problem. The obtained results are thoroughly analysed to illustrate the impact of drug resistance on the treatment. The capability of optimised schedules to deal with parametric uncertainty is also analysed. The drug schedules obtained in this work align well with the clinical standards. It is also revealed that the NSGA-ii optimised drug schedule with proper rest period between successive dosages yields the minimum cancer load at the end of the treatment.

关键词： Cancer chemotherapy Drug resistance Cell cycle specificity non-dominated sorting genetic algorithm ii

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Physical properties optimization of nerve guidance conduits utilizing genetic algorithm: A CFD analysis

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RESULTS IN ENGINEERING 2024年 21卷

作者： Nazarrashidi, Kiarash Rassoli, Aisa Sharbatdar, Mahkame KN Toosi Univ Technol Dept Mech Engn Tehran Iran

Nerve guidance conduits, or NGCs, used as structures in tissue engineering, play an important role in reconstruction and the growth of axons after nerve injuries. To produce an appropriate scaffold for nerve growth and transfer the suitable amount of growth factors, these channels should be designed and built with topological, mechanical, and biological properties and functional mass transfer requirements. This study deals with a computational design approach of a nerve guidance conduit which, after validating the geometry, simulates the blood flow utilizing the Carreau-Yasuda model and optimizes physical properties. The physical properties are hydraulic pressure, porosity, diffusivity and water absorption. The optimization process consists of three distinct steps: initially, a single objective function is implemented using a genetic algorithm (GA) to determine the optimal hydraulic pressure for the nerve guidance conduit, resulting in a final value of 0.039 Pa. Subsequently, the GA is employed again to optimize porosity, leading to a final value of 0.6473. Finally, a multi-objective function is utilized to consider porosity, diffusivity, and water absorption, simultaneously.

关键词： Nerve guidance conduit Nerve injury CFD Optimization non-Newtonian model genetic algorithm non-dominated sorting genetic algorithm ii

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Multi-objective Optimization Approach for Allocation of Electric Vehicles Parking Lots and Smart Charging with Distributed Energy Resource

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2023年第5期34卷 1070-1079页

作者： Ferraz, Rafael S. F. Ferraz, Renato S. F. Rueda-Medina, Augusto C. Fardin, Jussara F. Univ Fed Espirito Santo Postgrad Program Elect Engn Fernando Ferrari BR-29075910 Vitoria ES Brazil

Electric vehicles (EVs) are a promising solution to resolve the greenhouse emission issue as well as the fossil fuels scarcity problem in the future. However, as the number of EVs significantly increases, the excessive charging demand of EV parking lots (EVPLs) in the charging station may incur an instability problem to the electricity network. In this paper, the optimal allocation and sizing of EVPLs were carried out in order to minimize the costs of the EVPLs installation and EV users' distance traveled and recharge. As a consequence, it encourages the participation of vehicle users in a demand response program related to smart charging and time of use tariffs. It is important to mention that the increasing inclusion of EVs negatively affects the voltage level and power losses of the distribution system. Therefore, this paper also performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate these problems. The non-dominated sorting genetic algorithm ii (NSGA-ii) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 33-node test feeder and 25-node transportation network. Furthermore, it was considered a spatial distribution of EVs for the 24-h from the closeness centrality of graph theory. The optimal planning of EVPLs and DERs promoted a 35%, 23.14% and 34.11% reduction of the travel distance, voltage deviation and power losses, respectively.

关键词： Charging scheduling Distributed energy resource Electric vehicle parking lot non-dominated sorting genetic algorithm ii

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genetic algorithm-based optimisation method for product family design with multi-level commonality

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JOURNAL OF ENGINEERING DESIGN 2008年第5期19卷 401-416页

作者： Huang, George Q. Li, Li Schulze, Lothar Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China Leibniz Univ Hannover Dept Planning & Controlling Warehouse & Transport Hannover Germany

An increasing number of companies design a family of product variants simultaneously in order to introduce adequate product variety in the competitive market in a cost-effective way while shortening product lead-times, instead of designing one product at a time. The key to using this approach successfully is to achieve the right trade-off between product family commonality and performance of individual product variants. This paper considers multi-level commonality in product family design in the sense that the feature or component can be common only among some product variants. This differs from the two extremes of being totally common throughout the family or being completely different from one product variant to another. A product family design model is proposed as a multi-objective optimisation. A commonality index is introduced to evaluate the family commonality in the presence of multiple levels. A multi-objective genetic algorithm is developed for simultaneous design of a family of product variants. Optimal decisions include which design variables should be common among which product variants. Computational experiments are conducted using the design of a family of welded beams to demonstrate the effectiveness of the product family design method proposed in this paper.

关键词： mass customisation multi-level commonality product family genetic algorithm non-dominated sorting genetic algorithm ii

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Multi-mode resource-constrained project scheduling problem along with material ordering under time-of-use electricity tariffs and carbon taxes

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ANNALS OF OPERATIONS RESEARCH 2024年 1-45页

作者： Pouramin, Maryam Mirzazadeh, Abolfazl Davari-Ardakani, Hamed Mosadegh, Hadi Alajegerdi, Edris Kharazmi Univ Fac Engn Dept Ind Engn Tehran Iran Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran Univ Neyshabur Fac Sci Dept Phys Neyshabur Iran

The research highlights the potential of integrating energy management and the Multi-Mode Resource-Constrained Project Scheduling Problem, which includes material ordering. It addresses the pressing concerns of escalating electricity consumption and greenhouse gas emissions by considering renewable energy resources, time-of-use (TOU) electricity tariffs, and carbon taxes. In this regard, a mixed integer linear programming model is developed. To solve the small-sized problem instances, epsilon-constraint method is used. However, since the problem under consideration is NP-hard and exact methods fail to provide solutions for large-sized instances within a reasonable timeframe, two multi-objective meta-heuristic algorithms, non-dominated sorting genetic algorithm ii (NSGA-ii) and Multi-Objective Particle Swarm Optimization (MOPSO), are used as solution methods. Comparing results obtained by NSGA-ii and MOPSO demonstrates the superiority of NSGA-ii in the majority of performance metrics, regardless of the problem size. Through comprehensive sensitivity analysis of key model parameters, including TOU electricity tariffs and carbon tax rates, the research determines the optimal project duration and cost. Sensitivity analyses offer insights for sustainable project planning, highlighting the role of renewables in electricity consumption. Minimizing carbon taxes and proactive resource management underscore environmental responsibility, while TOU tariffs enhance sustainable energy practices. Overall, strategically integrating renewables in certain scenarios optimizes project execution, aligning with sustainability objectives for efficient and successful outcomes.

关键词： Multi-mode resource constrained project scheduling problem Material ordering Electricity cost Carbon dioxide emission non-dominated sorting genetic algorithm ii Multi-objective particle swarm optimization algorithm

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